Chemical reactors typically react one or more feedstocks in the presence of a catalyst system and other materials to produce a wide variety of chemical products. The reactors typically operate in the liquid or gas phase within a set of operating conditions such as temperature, pressure, and catalyst concentration to optimize the quantity or properties of the reactor product.
Some reactor systems can be controlled by monitoring indicators of important operating conditions. When an undesired trend or deviation is noted, an operator can intervene, altering one or more controllable parameters to attempt to return the reactor system to its desired operating conditions. Unfortunately, such rudimentary control systems typically are not practical for use with complex, continuous process reactors of the type employed in most modern chemical plants. Even where such a rudimentary control process can be used, the reactor operator's response is subjective, and can be too great, too small or, in the worst case, incorrect.
For this reason, modern chemical reactors typically employ a computer-based control system that continuously monitors and adjusts reactor parameters with great speed and accuracy. These modern control systems can operate in a manner analogous to the cruise control system of an automobile, allowing the control system to maintain an optimal reactor operating state without operator intervention, and permitting an operator to indicate a desired change in the operating state to the control system without having to dictate the system changes required to reach that desired end-state.
One example of a modern chemical reactor control system is the distributed control system, or “DCS”, used to control chemical reactors such as those used for the gas-phase manufacture of a polyolefin such as polypropylene. The DCS monitors critical reactor parameters on a continuous basis, and employs a number of local regulatory control loops to alter operating conditions such as catalyst flow, propylene monomer flow, and hydrogen flow. The use of DCS in polypropylene production has resulted in the ability to reliably produce large quantities of high quality polypropylenes. Nevertheless, the inherent limitations of local regulatory control loops and their associated control logic leave room for improvement in reactor control.
To further improve the performance of modern polyolefin reactors, plant engineers have turned to advanced process control systems, or “APC's”, to optimize control of the reactor system. These APC's employ state of the art modeling techniques such as neural networks, partial least squares, principle component regression or first principles models to infer impending changes of reactor state variables, and can then provide anticipatory control signals to the DCS local control loops to maintain the reactor in the optimum state. Because the APC's can anticipate changes in reactor state variables, the reactor can be maintained nearer to its optimal operating conditions than if control is based solely on deviations noted in DCS trends and the resulting local loop control changes.
Despite the availability of such state of the art advanced control techniques, modern chemical plants fail to deliver product at the theoretical maximums of quality and quantity. What is needed is a means to improve the performance of chemical plants above and beyond that enabled by advanced reactor process control systems.